Recursive blind channel identification and equalization by ULV decomposition

Most eigenstructure-based blind channel identification and equalization algorithms with second-order statistics need SVD or EVD of the correlation matrix of the output signal. We show new algorithms based on QR factorization of the output data directly. A recursive algorithm is developed by updating a rank-revealing ULV decomposition. Compared with existing algorithms in the same category, our algorithm is computationally more efficient and numerically (potentially) more robust. The computation in each recursion of the recursive algorithm can be reduced to the order of O(m/sup 2/) under some simplifications, where m is the dimension of the received signal vector. Numerical simulations demonstrate the performance of the proposed algorithm.